a brief overview of computer vision jinxiang chai many slides are borrowed from james hays and steve...
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A Brief Overview of Computer Vision
Jinxiang Chai
Many slides are borrowed from James Hays and Steve Seitz
Grading summary for short report# 3• 80/132 people submitted reports this time
56 got a 1015 got a 93 got an 82 got a 73 got a 0
• A few people made grammatical mistakes and some forgot cover sheets, but overall the reports were well written.
• 2/3 of the students who received 0s submitted reports on your writing improvement lecture instead of Dr. Caverlee's lecture.
• The other student who got a 0 submitted a report on social computing that had nothing to do with Dr. Caverlee's lecture and clearly had put very little effort put into it.
What is Computer Vision?
What is Computer Vision?
• Computer vision is the science and technology of machines that see.
• Concerned with the theory for building artificial systems that obtain information from images.
• The image data can take many forms, such as a video sequence, depth images, views from multiple cameras, or
multi-dimensional data from a medical scanner
Computer Vision
Make computers understand images and video.
What kind of scene?
Where are the cars?
How far is the building?
…
Components of a computer vision system
Lighting
Scene
Camera
Computer
Scene Interpretation
Srinivasa Narasimhan’s slide
Computer vision vs human vision
What we see What a computer sees
Vision is really hard
• Vision is an amazing feat of natural intelligence
– Visual cortex occupies about 50% of Macaque brain– More human brain devoted to vision than anything else
Is that a queen or a
bishop?
Vision is multidisciplinary
From wiki
Why computer vision matters
Safety Health Security
Comfort AccessFun
A little story about Computer Vision
In 1966, Marvin Minsky at MIT asked his undergraduate student Gerald Jay Sussman to “spend the summer linking a camera to acomputer and getting the computer to describe what it saw”. We now know that the problem is slightly more difficult than that. (Szeliski 2009, Computer Vision)
A little story about Computer Vision
In 1966, Marvin Minsky at MIT asked his undergraduate student Gerald Jay Sussman to “spend the summer linking a camera to acomputer and getting the computer to describe what it saw”. We now know that the problem is slightly more difficult than that. (Szeliski 2009, Computer Vision)
Founder, MIT AI project
A little story about Computer Vision
In 1966, Marvin Minsky at MIT asked his undergraduate student Gerald Jay Sussman to “spend the summer linking a camera to acomputer and getting the computer to describe what it saw”. We now know that the problem is slightly more difficult than that. (Szeliski 2009, Computer Vision)
Image UnderstandingImage Understanding
Ridiculously brief history of computer vision
• 1966: Minsky assigns computer vision as an undergrad summer project
• 1960’s: interpretation of synthetic worlds
• 1970’s: some progress on interpreting selected images
• 1980’s: ANNs come and go; shift toward geometry and increased mathematical rigor
• 1990’s: face recognition; statistical analysis in vogue
• 2000’s: broader recognition; large annotated datasets available; video processing starts
Guzman ‘68
Ohta Kanade ‘78
Turk and Pentland ‘91
How vision is used now• Examples of state-of-the-art
Optical character recognition (OCR)
Digit recognition, AT&T labshttp://www.research.att.com/~yann/
Technology to convert scanned docs to text• If you have a scanner, it probably came with OCR software
License plate readershttp://en.wikipedia.org/wiki/Automatic_number_plate_recognition
CAPTCHA
CAPTCHA stands for "Completely Automated Public Turing test to Tell Computers and Humans Apart".
Picture of a CAPTCHA in use at Yahoo.
http://www.cs.sfu.ca/~mori/research/gimpy/
Breaking a Visual CAPTCHA
http://www.cs.sfu.ca/~mori/research/gimpy/
On EZ-Gimpy: a success rate of 176/191=92%!
Other exampleshttp://www.cs.sfu.ca/~mori/research/gimpy/
Breaking a Visual CAPTCHA
http://www.cs.sfu.ca/~mori/research/gimpy/
On more difficult Gimpy: a success rate of 33%!
Other exampleshttp://www.cs.sfu.ca/~mori/research/gimpy/hard/
Breaking a Visual CAPTCHA
YAHOO’s current CAPTCHA format
http://en.wikipedia.org/wiki/CAPTCHA
Face detection
• Many new digital cameras now detect faces– Canon, Sony, Fuji, …
Smile detection
Sony Cyber-shot® T70 Digital Still Camera
Object recognition (in supermarkets)
LaneHawk by EvolutionRobotics“A smart camera is flush-mounted in the checkout lane, continuously watching for items. When an item is detected and recognized, the cashier verifies the quantity of items that were found under the basket, and continues to close the transaction. The item can remain under the basket, and with LaneHawk,you are assured to get paid for it… “
Vision-based biometrics
“How the Afghan Girl was Identified by Her Iris Patterns” Read the story wikipedia
Login without a password…
Fingerprint scanners on many new laptops,
other devices
Face recognition systems now beginning to appear more widely
http://www.sensiblevision.com/
Object recognition (in mobile phones)
Point & Find, NokiaGoogle Goggles
The Matrix movies, ESC Entertainment, XYZRGB, NRC
Special effects: shape capture
Pirates of the Carribean, Industrial Light and Magic
Special effects: motion capture
Sports
Sportvision first down lineNice explanation on www.howstuffworks.com
http://www.sportvision.com/video.html
Smart cars
• Mobileye [wiki article]– Vision systems currently in high-end BMW, GM,
Volvo models – By 2010: 70% of car manufacturers.
Slide content courtesy of Amnon Shashua
Google cars
http://www.nytimes.com/2010/10/10/science/10google.html?ref=artificialintelligence
Interactive Games: Kinect• Object Recognition: http://www.youtube.com/watch?feature=iv&v=fQ59dXOo63o• Mario: http://www.youtube.com/watch?v=8CTJL5lUjHg• 3D: http://www.youtube.com/watch?v=7QrnwoO1-8A• Robot: http://www.youtube.com/watch?v=w8BmgtMKFbY
• 3D tracking, reconstruction, and interaction: http://research.microsoft.com/en-
us/projects/surfacerecon/default.aspx
Vision in space
Vision systems (JPL) used for several tasks• Panorama stitching• 3D terrain modeling• Obstacle detection, position tracking• For more, read “Computer Vision on Mars” by Matthies et al.
NASA'S Mars Exploration Rover Spirit captured this westward view from atop a low plateau where Spirit spent the closing months of 2007.
Industrial robots
Vision-guided robots position nut runners on wheels
Mobile robots
http://www.robocup.org/NASA’s Mars Spirit Rover
http://en.wikipedia.org/wiki/Spirit_rover
Saxena et al. 2008STAIR at Stanford
Medical imaging
Image guided surgeryGrimson et al., MIT
3D imagingMRI, CT
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